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The aim of optical motion capture is to sequentially estimate the true state X of the subject (generally an articulated body) at any time instant tk from a set of data Dk, captured by N calibrated cameras each of resolution U×V pixels. Our aim is to achieve this without the need for markers. This is to enhance both utility and portability for the motion capture system and to render it suitable for surveillance issues. Even though several stochastic techniques to address this issue exist, we aim to solve this problem in a deterministic way, for future real-time performance. We adopt a Bayesian framework under which we employ a 3D articulated model M and a rendering function Λ to describe the data. Unlike other existing approaches [R. Urtasun et al. (2004), J. Deutscher et al. (2000)], we solely use silhouette matching to obtain a measure of how well the model describes the data.
Image Processing, 2004. ICIP '04. 2004 International Conference on (Volume:4 )
Date of Conference: 24-27 Oct. 2004